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R E S E A R C H A R T I C L E

Open Access

Genomic reconstruction of transcriptional

regulatory networks in lactic acid bacteria

Dmitry A Ravcheev

1,2

, Aaron A Best

3

, Natalia V Sernova

2

, Marat D Kazanov

2

, Pavel S Novichkov

4

and Dmitry A Rodionov

1,2*

Abstract

Background: Genome scale annotation of regulatory interactions and reconstruction of regulatory networks are the crucial problems in bacterial genomics. The Lactobacillales order of bacteria collates various microorganisms having a large economic impact, including both human and animal pathogens and strains used in the food industry. Nonetheless, no systematic genome-wide analysis of transcriptional regulation has been previously made for this taxonomic group.

Results: A comparative genomics approach was used for reconstruction of transcriptional regulatory networks in 30 selected genomes of lactic acid bacteria. The inferred networks comprise regulons for 102 orthologous transcription factors (TFs), including 47 novel regulons for previously uncharacterized TFs. Numerous differences between regulatory networks of the Streptococcaceae and Lactobacillaceae groups were described on several levels. The two groups are characterized by substantially different sets of TFs encoded in their genomes. Content of the inferred regulons and structure of their cognate TF binding motifs differ for many orthologous TFs between the two groups. Multiple cases of non-orthologous displacements of TFs that control specific metabolic pathways were reported.

Conclusions: The reconstructed regulatory networks substantially expand the existing knowledge of transcriptional regulation in lactic acid bacteria. In each of 30 studied genomes the obtained regulatory network contains on average 36 TFs and 250 target genes that are mostly involved in carbohydrate metabolism, stress response, metal homeostasis and amino acids biosynthesis. The inferred networks can be used for genetic experiments, functional annotations of genes, metabolic reconstruction and evolutionary analysis. All reconstructed regulons are captured within the Streptococcaceae and Lactobacillaceae collections in the RegPrecise database (http://regprecise.lbl.gov). Keywords: Transcriptional regulatory network, Comparative genomics, Carbohydrate metabolism, Lactobacillaceae, Streptococcaceae, Lactic acid bacteria, Regulon, Transcription factor

Background

Regulation of gene expression in response to external and internal stimuli is a crucial mechanism for adaptation of microorganisms to changes of environmental conditions and intracellular states. In Bacteria, regulation of gene ex-pression at the transcriptional level is usually mediated by transcription factors (TFs) that recognize their cognate TF-binding sites (TFBSs) in the promoter regions of regulated genes. A set of target genes under direct control of a certain

TF is defined as a regulon. All regulons in a single organism establish the transcriptional regulatory network (TRN), a fine-tuned system for complex regulation of gene expres-sion in response to environmental changes and physio-logical needs of the cell.

Reconstruction of TRNs in bacterial genomes involves identification of regulatory interactions between target operons and TFs that requires genome-wide definition of all respective TFBSs. Various approaches for TRN recon-struction have been developed including traditional bottom-up genetic methods [1-3] and new top-down tech-niques based on the large-scale expression data [4] and/or automated inference of TFBS motifs [5-8]. On the other hand, the growing number of available complete genomic * Correspondence:rodionov@burnham.org

1Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA 2

A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow 127994, Russia

Full list of author information is available at the end of the article

© 2013 Ravcheev et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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sequences opens opportunities for comparative genomic analysis of transcriptional regulation and subsequent TRN inference (reviewed in [9,10]). This analysis can be effi-ciently used for both propagation of known regulons to previously uncharacterized organisms and for ab initio dis-covery of novel regulons. Combination of the comparative genomic-based regulon reconstruction with other genome context analysis techniques and multiple available experi-mental datasets define a knowledge-driven approach for reconstruction of TRNs in a set of related bacterial gen-omes. This approach has been successfully used for TRN inference in two groups of related genomes including 16

Shewanella species [11] and six species from the

Staphylococcaceae family [12]. The comparative genomics approach not only allowed us to reconstruct TRNs in mul-tiple genomes but also resulted in prediction of functions for previously uncharacterized genes.

In this study, we applied the knowledge-driven com-parative genomic approach for reconstruction of TRNs in lactic acid bacteria belonging to the Lactobacillales order of the Firmicutes phylum. In spite of the large number of complete genomes and huge economic im-pact of this group (reviewed in [13-15]), most studies of gene regulation in these bacteria are limited to individual regulons in some model species; large-scale TRN recon-structions were attempted for Lactobacillus plantarum [16] and Lactococcus lactis [17]. On the other hand, availability of complete genomic sequences and multiple experimental data on gene regulation provide an oppor-tunity for application of comparative genomic-based techniques for reconstruction of TRNs in lactic acid bac-teria. To reconstruct genome-wide TRNs in the set of 30

Lactobacillales genomes we used a modified approach

with three innovations. First, we subdivided the analyzed group of genomes into two phylogenetically distinct groups: the Streptococcaceae group including species from the Streptococcus and Lactococcus genera, and the Lactobacillaceae group that also includes two closely-related genomes from the Leuconostocaceae family. Second, to compose the initial training sets of TF-regulated genes we used the available experimental data on transcriptional regulation from 14 model species. Third, the coordinated reconstruction of a large number of TF regulons was carried out by a community of anno-tators using the RegPredict platform [18] with subse-quent curation and quality control. Using this combined approach we reconstructed regulons for 102 orthologous TFs, including 47 novel regulons predicted for the first time in this study and awaiting further experimental validation.

Results and discussion

We selected a set of 30 complete genomes in the

Lacto-bacillales order for TRN reconstruction. Based on the

phylogenetic species tree (Additional file 1) all studied genomes were divided into two groups called the Streptococcaceaeand Lactobacillaceae. The Streptococca-ceae family includes 13 Streptococcus spp. and 2 strains of Lactococcus lactis. The second group includes 13 genomes from the Lactobacillaceae family and 2 genomes form the Leuconostocaceae family that are phylogenetically close to each other [19].

Repertoire of TFs in Lactobacillales genomes

To estimate the scale and diversity of the TF mediated regulatory networks in the Lactobacillales genomes, we performed a genetic census of their putative DNA-binding TFs using similarity search and the existing prokaryotic TF compilations (Additional files 2, 3). The total number of putative TFs varies broadly within the

Lactobacillales genomes, from ~60 in S. thermophilus

and L. helveticus to ~240 in L. plantarum and ~150 in S. gallolyticus(Additional file 4).

The putative TFs identified in the Lactobacillales are distributed between 49 protein families and about 90% of these TFs belong to 24 major families with at least two representatives per genome. The largest number of TF representatives was observed for the Xre family (298 TFs total, ~19 TFs per genome). Among other large families with more than 4 TFs per genome are the TetR, GntR, MarR, OmpR, LacI, LysR, MerR and AraC fam-ilies. Comparison of TF repertoires between the two Lactobacillales groups reveals 42 TF families that have representatives in both groups. Among the lineage-specific TF families, three families (CodY, PF04394, YobV) are present only in the Streptococcaceae, whereas four families (LexA, SdaR, SfsA, ComK) are unique for the Lactobacillaceae. Interestingly, the LexA and CodY regulators are both present in the Staphylococcaceae, Bacillaceae, and Enterococcaceae families of Firmicutes, suggesting the family-specific loss of these TFs and their regulons in the Streptococcaceae and Lactobacillaceae families, respectively.

The entire set of 3445 TFs identified in 30 studied genomes was broken into 596 orthologous groups in the Streptococcaceaeand 640 orthologous groups in the Lac-tobacillaceae(Additional files 2, 3). The numbers of uni-versal TFs present in all analyzed Streptococcaceae and

Lactobacillaceae genomes are 21 and 8, respectively

(Figure 1). At that, only 5 regulators are shared between these two groups of universal TFs including the global regulator for catabolite repression CcpA [20,21], the copper uptake regulator CopR [22], the purine biosyn-thesis regulator PurR [23], the redox control global regu-lator Rex [12], and the aminosugar utilization reguregu-lator NagR [24]. In each taxonomic group we defined all TFs that are present in more than half of the analyzed gen-omes as conserved TFs. Among the conserved TFs, 47

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are shared by both studied lineages, whereas the Strepto-coccaceaeand Lactobacillaceae groups have respectively 22 and 12 conserved TFs that are lineage-specific. The regulators of carbohydrate and amino acid metabolism form the most populated functional group of conserved TFs (Figure 1).

The studied two taxonomic groups of lactic acid bac-teria demonstrate different distributions of orthologous TFs. The fractions of universal and conserved TFs are sig-nificantly higher in the Streptococcaceae than in the Lactobacillaceae. On the other hand, the Lactobacillaceae genomes are equipped by a confidently higher fraction of sporadically distributed TFs that are present in 2 to 6 gen-omes. Thus, the Lactobacillaceae are characterized by higher variability of TFs orthologous groups than the

Strep-tococcaceae that is in correspondence with the larger

phylogenetic distances between species in the Lactobacilla-ceaefamily (Additional file 1).

Reconstruction of regulons in two lineages of Lactobacillales

A comparative genomic approach implemented in the RegPredict Web server [18] was applied for regulon in-ference in the Streptococcaceae and Lactobacillaceae groups of genomes. Totally, 102 orthologous TF regu-lons were reconstructed in the studied genomes (Additional file 5). Initially we collected the published experimental data on transcriptional regulation in model Lactobacillalesspecies (Additional file 5). Depending on the availability of experimental data, we applied three different workflows for regulon inference: (1) expansion and projection of TF regulons previously characterized

in Lactobacillales, (2) the reconstruction of regulons for TFs that have orthologs previously characterized in B. subtilis or S. aureus, and (3) ab initio inference of regu-lons for previously uncharacterized TFs (Figure 2).

A significant number of the studied Lactobacillales genomes, including eight Streptococcus, two Lactococcus and four Lactobacillus species, were considered as model species with previously characterized TFs and regulons (Additional file 5). By using workflow 1, we propagated the previously established regulatory interac-tions for 41 TFs in the model Lactobacillales species, and predicted new regulon members by the comparative genomics approach. Using workflow 2, we inferred regu-lons for twelve TFs that have orthologs previously ex-perimentally investigated in B. subtilis and two TFs that were previously studied in S. aureus. Finally, using work-flow 3, we predicted and described 47 novel TF regulons (Figure 2). Thus, all TF regulons studied in this work are either entirely predicted by computational analysis regu-lons or partially predicted reguregu-lons with some regulatory interactions supported by experimental data in model species.

The resulting set of reconstructed regulons varies drastic-ally between the individual genomes (Table 1). The average number of reconstructed TF regulons per genome is 35.8, whereas the minimal and maximal numbers are 18 and 46 that were inferred in L. delbrueckii and L. plantarum, re-spectively (Figure 3). An average regulatory network of the reconstructed TF regulons includes ~250 genes per genome. The minimal number of genes in a reconstructed TRN was observed in L. delbrueckii (69 genes), whereas the maximal number of regulated genes in a network was in S. 5 4 3 1 8 4 4 3 21 8 4 6 8 5 7 8 11 7 11 15 29 42 32 75 7 1 1 122 325 312 28 350 300 250 200 150 100 50 0 1 1 15 14 13 12 11 10 9 8 7 6 5 4 3 2 1 Orthologous groups 3 16 5 22 47 12 Streptococcaceae Lactobacillaceae (596 groups) 640 ( groups) (A) (B)

Figure 1 Distribution of TF orthologous groups in studied genomes. (A) Distribution of TF orthologous groups in genomes. Conserved TFs are present in more than half analyzed genomes within the lineage. Universal TFs are present in all genomes of the lineage. (B) Examples of functional annotations for conserved TFs. TFs for which regulons were reconstructed are shown in bold, other TFs are underlined. TFs universally conserved in at least one lineage of the Lactobacillales are highlighted in green.

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suis (366 genes). In summary, the number of regulated genes in the reconstructed TF regulons is significantly higher in the Streptococcaceae genomes. However, L.

plantarum from the Lactobacillaceae group has numbers

of TFs and target genes in the reconstructed TRN compar-able with Streptococcaceae. The observed variability in reconstructed TRNs of studied microorganisms can be explained by multiple factors including the diversity of their respective ecological niches and nutrient availability, and different metabolic capacities of individual species.

Classification of reconstructed regulons by taxonomic distribution

Distribution and properties of 102 TF regulons in 30 genomes of the Lactobacillales are summarized in Additional file 5. In each of the two taxonomic groups, the Streptococcaceae and Lactobacillaceae, we defined TF regulons that are universal (i.e. present in all 15 genomes) and the remaining regulons with a mosaic dis-tribution in the analyzed genomes (Table 2). Only five regulons including the global regulons CcpA and Rex were found to be universally conserved in both lineages. Ten regulons that are universal in the Streptococcaceae

have orthologs with mosaic distribution in the

Lactobacillaceae. In contrast, no TF regulons appeared in the group of regulons that are universal in the Lactobacillaceae but mosaic in the Streptococcaceae. A large set of 31 TF regulons that are mosaic in both lineages contains regulons controlling different sugar utilization pathways.

The remaining TF regulons reconstructed in this work are present only in a single taxonomic group. Among 23 TF regulons present solely in the Streptococcaceae, the CmbR, CodY and PipR regulons are universal, whereas the others have a mosaic distribution in 15 analyzed genomes. The SOS response regulon LexA is only linage-specific reg-ulon that was identified as universal in the Lactobacillaceae, whereas the remaining 20 lineage-specific TF regulons have a mosaic distribution in this lineage.

In conclusion, 23 TF regulons are specific for the Streptococcaceae, 33 regulons are present only in the Lactobacillaceaeand 46 regulons have orthologs in both lineages. Thus, among the reconstructed regulons, the Streptococcaceae group has significantly larger number of universal TF regulons in comparison with the Lactobacillaceaegroup. inferred regulons 47 TFs Streptococcaceae Lactobacillaceae 12 21 14 or 14 TFs Streptococcaceae Lactobacillaceae 5 7 2 35 TFs Streptococcaceae Lactobacillaceae 25 4 6 6 TFs Streptococcaceae Lactobacillaceae 4 1 1 in Lactobacillales 41 TFs Streptococcaceae Lactobacillaceae 29 5 7

102 TFs

Streptococcaceae Lactobacillaceae 46 33 23

Figure 2 Workflows used for regulon inference. Venn diagrams show numbers of regulons reconstructed by every approach: white background, regulons shared by both lineages; blue background, Streptococcaceae specific regulons; red background, Lactobacillaceae specific regulons.

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Classification of reconstructed regulons by TF protein families

The reconstructed regulons are controlled by TFs from 31 protein families. The mostly represented TF family in the obtained set of regulons is the LacI family (26 regulons). As expected, all LacI family regulons reconstructed in this work controls different carbohydrate utilization pathways. Another broadly represented TF family is GntR family (13 regulons) that control carbon and amino acid metabo-lisms and resistance to toxic compounds. TFs from the

TetR and MarR families (7 and 6 reconstructed regulons, respectively) regulate genes involved in environmental adaptation, multidrug and heavy metal resistance and fatty acids metabolism. The reconstructed regulons from the RpiR family control various carbohydrate utilization path-ways (5 regulons), whereas the predicted AguR regulon controls the agmatine utilization pathway. The BglR and DeoR families of TFs include respectively 6 and 5 regulators that control carbohydrate catabolism. Three regulons oper-ated by TFs from the LysR family regulate the cysteine/ Table 1 Statistics for reconstructed regulons in studied genomes

Genome TFs Target genes Target operons Regulatory interactions

Streptococcaceae L. lactis cremoris SK11 36 255 125 130

L. lactis lactis Il1403 34 244 128 138

S. thermophilus CNRZ1066 30 263 125 141 S. agalactiae 2603 V/R 38 340 159 186 S. uberis 0140 J 42 330 156 183 S. equi MGCS10565 42 334 143 167 S. dysgalactiae GGS_124 43 356 160 189 S. pyogenes M1 GAS 40 319 150 180 S. gallolyticus UCN34 41 328 167 199 S. mutans UA159 41 317 147 173 S. suis 05ZYH33 43 366 145 173 S. mitis B6 35 305 148 174 S. pneumoniae TIGR4 42 365 167 206 S. gordonii CH1 41 312 167 194 S. sanguinis SK36 43 339 163 191 Total 591 4773 2250 2624 Non-overlapping1 69 779 397 470 Lactobacillaceae L. sakei 23 K 36 186 92 106 L. casei ATCC 334 41 226 110 120 L. rhamnosus GG 42 237 106 116

L. delbrueckii ATCC BAA-365 18 69 36 37

L. acidophilus NCFM 27 165 80 90 L. helveticus DPC 4571 21 91 53 55 L. johnsonii NCC 533 26 145 78 87 P. pentosaceus ATCC 25745 38 205 97 111 L. brevis ATCC 367 39 217 112 128 L. plantarum WCFS1 46 299 147 170 L. fermentum IFO 3956 30 172 85 101 L. reuteri JCM 1112 32 167 83 96 O. oeni PSU-1 25 109 59 70 L. mesenteroides ATCC 8293 32 202 89 103 L. salivarius UCC118 31 198 86 96 Total 484 2688 1313 1486 Non-overlapping1 79 539 289 328 1

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methionine metabolism, whereas one regulon (MleR) con-trols the malonate utilization pathway.

Classification of reconstructed regulons by function The functional gene content was assessed for each recon-structed TF regulon to tentatively predict its possible biological function and molecular effector. Metabolic re-construction of the respective biochemical pathways and prediction of functions of co-regulated genes were per-formed using the subsystem-based approach implemented in the SEED genomic platform [25,26]. The inferred func-tional annotations of genes constituting regulons were cap-tured in the RegPrecise database [27] within the taxonomic

collections of regulons for the Streptococcaceae and Lactobacillaceae groups (http://regprecise.lbl.gov/RegPre-cise/collections_tax.jsp).

Overall, the reconstructed regulons were classified into 8 functional groups (Additional file 5). The largest group counts 56 regulons for carbohydrate and central carbon metabolism. Three other functional groups of regulons are involved of stress response (11 regulons), metal homeosta-sis (9 regulons) and amino acid metabolism (8 regulons). Small numbers of regulons were reconstructed for cofactor metabolism (4 regulons), nucleotide metabolism (3 regu-lons) and fatty acid metabolism (2 reguregu-lons). Additionally, 9 reconstructed TF regulons contain genes with unknown

50 45 40 35 30 25 20 15 10 5 0 0 50 100 150 200 250 300 350 400 Streptococcaceae Lactobacillaceae Target genes in TRN TFs in TRN

Figure 3 Scatter plot for numbers of TFs and target genes in studied genomes. Genomes with minimal and maximal numbers of target genes in each lineage are signed.

Table 2 Distribution of TFs with reconstructed regulons for orthologous TFs inStreptococcaceae and Lactobacillaceae genomes

TF type1 TF

number

Examples Streptococcaceae Lactobacillaceae

Universal Universal 5 CcpA, CopR, NagR, PurR, Rex

Universal Mosaic 10 AdcR, ArgR, BirA, CtsR, FabT, FruR, GlnR, HrcA, MalR, NrdR

Mosaic Universal 0 n/a

Mosaic Mosaic 31 CcpB, CelR, FucR, GalR, GutR, LacR, MdxR, MleR, MntR, MtaR, MtlR, MurR, NiaR, NrtR, PadR, PerR, PflR, ScrR, TagR, TreR, UxuR

Universal ― 3 CmbR, CodY, PipR

Mosaic ― 20 AgaR, AlsR, CelQ, HomR, NanR, NmlR, PdxR, RegR, Rgg, RgrA, RliC, SczA, SgaR

― Universal 1 LexA

― Mosaic 32 AguR, AraR, CggR, DeoR, ExuR, FatR, HxlR, IolR, NihR, RbsR, RpiR, SdaR, XylR, Zur

Numbers of regulons for orthologous TFs in Streptococcaceae and Lactobacillaceae are shown.1‘Universal’ regulons are present in all 15 studied genomes of the

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or hypothetical functions, and thus their specific functional roles and effectors remain unknown. Functional content for the selected subset of reconstructed regulons is briefly described below.

Carbohydrate metabolism

Carbohydrates comprise a key source of carbon and energy for a variety of microorganisms. Genomes from the Lacto-bacillalesorder encode a large number of sugar catabolic pathways and most of these pathways have a mosaic distri-bution in individual species [28]. This diversity of sugar catabolic pathways is matched by a large number of regulatory systems that allow sugar-specific induction of ex-pression of these pathways.

Here we reconstructed 56 TF regulons that control the sugar and central carbon metabolism in the Lactobacillales. In addition to 19 sugar metabolism reg-ulons that were previously characterized experimentally in model Lactobacillales species, 6 regulons were recon-structed by projection from B. subtilis or S. aureus and 31 novel regulons were predicted for the first time in this work (Additional file 5). Analysis of available experi-mental data revealed 114 previously known regulatory interactions involving the sugar metabolism regulators in model Lactobacillales organisms. Using comparative genomics, we were able to significantly expand the sugar metabolism regulatory subnetwork. As an example, the previously known regulator of hyaluronidase RegR in S. pneumoniae[29] was predicted to have an expanded reg-ulon of 12 additional genes involved in the hyaluronate utilization that is conserved in six Streptococcaceae genomes (Additional file 6).

About 10 new regulatory interactions per genome were predicted for carbohydrate metabolism genes using the pro-jection of known regulons from B. subtilis and S. aureus. For example, CcpN in B. subtilis was previously described as a regulator of the gluconeogenesis genes gapB and pckA [30]. The comparative genomics reconstruction of the CcpN regulon revealed the pyruvate phosphate dikinase ppdK, the ccpN gene and the fructose biphosphatase fbp as novel members of the CcpN regulon in the Lactobacillales (Additional files 6, 7). Thus, the novel CcpN regulon in the Lactobacillaleshave a set of target genes that is completely different from the known CcpN regulon in B. subtilis, al-though in both lineages it controls the gluconeogenesis pathway.

The reconstructed regulatory network includes 52 TFs that control 38 peripheral sugar utilization pathways, and 10 of these pathways are controlled by more than one TF. For instance, we reconstructed 5 different TF regulons for maltose and maltodextrin utilization pathway and 3 TF reg-ulons for the sucrose catabolism. Two different TF regreg-ulons per one sugar metabolic pathway were described for path-ways involved in utilization of ascorbate, cellobiose,

gluconate, lactose, N-acetylgalactosamine, ribose and tre-halose. The observed redundancy in sugar-specific TFs is explained by (i) non-orthologous replacements of TFs for the same pathway in different genomes, and (ii) existence of alternative pathway variants and multiple paralogs regu-lated by different TFs in the same genome. For example, two maltose/maltodextrin ABC transporters are controlled by two non-orthologous TFs from the LacI family in the Streptococcaceae(Additional file 8). The malEFG operon is always regulated by MalR protein. The malXCDA operon is regulated by MalR in four Streptococcus spp. and under control of MalR2 in three other Streptococcus spp.

Three other sugar utilization pathways in the

Streptococcusspp. are equipped by a redundant set of cata-bolic genes controlled by multiple non-orthologous regula-tors. SgaR and SgaR2 from the BglG family control the ascorbate utilization. The GntR family regulators AgaR and AgaR2 were predicted to control the N-acetylgalactosamine utilization gene loci. The cellobilose utilization genes are regulated by CelR from the BglG family [31] and CelQ from the ROK family [32]. These functionally redundant sets of TF regulons indicate a complex evolutionary history of the sugar utilization subsystems in Firmicutes.

Stress response

We reconstructed totally 11 TF regulons involved in vari-ous stress responses and drug resistance. These include the CtsR, HrcA, NmlR, PadR and PerR regulons that were pre-viously experimentally described in at least one model

Lactobacillales genome. The SOS response regulon LexA

and multidrug resistance regulon YtrA were projected using the previous knowledge of orthologous regulons in B. subti-lis [33,34]. Overall, we predicted 13 target operons in the LactobacillaceaeLexA regulon including some novel func-tions (e.g. parEC, addBA, nrnA).

The Fur family regulator PerR was previously studied in S. pyogenes and S. suis, where it co-regulates genes involved in peroxide stress response and manganese transport [35,36]. Here we predicted some new members of the PerR regulon involved in iron transport (fhuADBG, feoAB, fatDCAB) and iron-sulfur cluster biosynthesis (sufCDSEB). The latter iron-sulfur cluster biosynthesis op-eron was previously identified by us as a novel member of the PerR regulon in the Staphylococcaceae family [12]. Interestingly, we also predicted that in some Streptococcus spp., PerR controls expression of another TF for manganese homeostasis, MntR, and these two regulators form a poten-tial cascade.

Metal homeostasis

The group of reconstructed regulons for metal homeostasis includes 9 TF regulons. Starting from 24 regulatory interac-tions previously described in 5 known regulons for model Lactobacillalesorganisms, we expanded and projected the

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AdcR, CopR, FlpA, MntR and SczA regulons resulting in prediction of several new regulatory interactions in the gen-omes. For instance, we predicted regulation of genes encoding manganese (mntH) and nickel (nikABCDE) transporters, heavy metal-transporting ATPase (pmtA), and

mntR gene by the manganese homeostasis regulon MntR

in the Streptococcaceae.

In contrast to the AdcR regulon for control of zinc homeostasis in the Streptococcaceae, the Lactobacillaceae species utilize the typical zinc-responsive regulator Zur from the Fur family. The Zur regulon in the Lactobacilla-ceae, which was reconstructed by projection from the B. subtilisZur regulon, includes the zinc transporter znuABC, and the zur and rpsN2 genes, the latter encoding a paralog of the ribosomal protein S14.

Two novel TFs from the ArsR family analyzed in the Lactobacillaceaeare distantly related to the CzcR regulator of cobalt-zinc-cadmium resistance from Pseudomonas aeruginosa[37]. The reconstructed CzcR1 and CzcR2 regu-lons were predicted to control different sets of genes involved in heavy metal resistance. Another novel regulon, termed NihR (nickel homeostasis regulator), was predicted by its conserved co-localization with the nikMQO genes en-coding a nickel transport system. The predicted NihR regu-lon also includes the nihR and nixA genes, the latter encoding a high-affinity nickel permease (Additional file 7). Amino acids metabolism

Among 8 reconstructed TF regulons for control of amino acid metabolism, seven regulons have been previ-ously described in at least one model Lactobacillales species. For these TF regulons, we projected 56 regula-tory interactions to additional genomes and predicted ~25 novel regulatory interactions per genome. First, we predicted the MtaR- and CmbR-dependent regulation of the mmuMP operon involved in S-methylmethionine utilization in the Streptococcaceae. Second, we identified multiple new members of the nitrogen metabolism regu-lon GlnR in both lineages including the glutamine transporter gluQHMP, the arginine catabolism genes

arcABCand the aspartate-ammonia ligase asnA. A novel

amino acid metabolism regulon that was inferred for the first time in this work is operated by a RpiR-like regula-tor, termed AguR (agmatine utilization regulator), which was found to be co-localized with agmatine utilization genes in the Lactobacillaceae genomes.

CRISPR-Cas genes under the control of global regulators CRISPR-Cas is a recently discovered prokaryotic RNA-based system for adaptive immunity for defense against phages, plasmids and other mobile genetic elements [38,39]. Previously, the expression of CRISPR-Cas genes has been shown to be regulated by different global TFs, such as H-NS, Lrp, LeuO [40,41] and Crp [42] in E. coli,

and Rex in Thermotoga maritima [43]. In this work we pre-dicted some new cases of regulation of CRISPR-Cas genes in the Lactobacillales genomes. The predicted operon cas9-cas1-cas2-csn2 is preceded by a candidate CcpA-binding site in S. agalactiae and S. mutans, whereas in S. agalactiae and S. equi this operon has a candidate CodY-binding site in its promoter region. Another putative CRISPR-Cas operon, cas5-cas8c-cas7-cas1-cas2, is predicted to be regu-lated by CcpA in S. equi and S. pyogenes. These predicted regulatory interactions for CRISPR-Cas genes extend our understanding of regulatory mechanisms for bacterial im-mune systems.

Evolution of regulons in Lactobacillales Evolution of orthologous TF regulons

Among 102 TF regulons reconstructed in this work, 46 regulons have orthologous TFs in both studied lineages of the Lactobacillales (Additional file 5). We compared the deduced TFBS motifs for the orthologous TF regulons in the Streptococcaceae and Lactobacillaceae lineages and classified them into three categories (Additional file 9). Cat-egory I includes 23 TF regulons with binding motifs that are well conserved or slightly variable in two lineages. Category II contains 17 TF regulons with moderately differ-ent motifs (2 to 4 mismatches in the conserved motif posi-tions). Category III has 6 remaining TF regulons with binding motifs that are substantially different between the Streptococcaceaeand Lactobacillaceae. Remarkably, the cat-egory I is enriched by universal and highly conserved regu-lators. Thus, it includes all 5 regulons that are universal in both studied lineages and 10 regulons for TFs that are universal in the Streptococcaceae and have a mosaic distri-bution in the Lactobacillaceae. The category I contains the highest number of global regulons (CcpA, Rex) and mid-size regulons that control 3 or more target operons per genome (AdcR, ArgR, CtsR, GlnR, HrcA, MalR, MtaR, PerR and PurR). The remaining 13 regulons in this category are local, i.e. containing less than 3 target operons per genome. In contrast, the categories II and III contain re-spectively 94% and 100% of local regulons. These observa-tions suggest that the conservation of TFBS motifs have a positive correlation with the regulon size. Similar correl-ation was previously reported for the Staphylococcaceae spp. [12].

Analysis of conservation of gene contents for orthologous TF regulons between the two studied lineages classified all regulons into three different categories. The first group contains 27 strictly conserved regulons that do not demon-strate any difference in their gene content or have only slight changes between the Streptococcaceae and Lactoba-cillaceae (e.g. an insertion of additional genes into target operons). The second group includes 16 regulons that have a common core of conserved genes supplemented by unique sets of peripheral genes that are substantially

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different between the Streptococcaceae and Lactobacilla-ceae . This group of partially conserved regulons includes the global regulons CcpA and Rex and several mid-size reg-ulons (see below). The third group contains 3 orthologous regulons, MdxR, NiaR and NrtR, having completely differ-ent sets of target genes between the Streptococcaceae and Lactobacillaceaegroups, although their corresponding bio-logical roles are conserved between the studied groups. High diversity of the latter three regulons can be explained by redundancy of the respective metabolic systems and regulatory mechanisms. Thus, MdxR controls the maltose and maltodextrin utilization and the similar biological role was assigned to several other TF regulons in the Lactobacil-lales (see above). The phylogenetic distributions of two NAD metabolism regulons NiaR and NrtR have an overlap-ping pattern in many studied genomes (Additional file 5). In conclusion, we did not observe any correlation between the conservation of orthologous TF regulon structure and their cognate binding motifs in the analyzed taxonomic groups.

The 16 TF regulons with a common core and flexible set of peripheral genes between the Streptococcaceae and Lactobacillaceaegroups can be classified into three sub-groups: (i) regulons expanded in the Streptococcaceae (AdcR, BirA, CopR, GlnR, LacR and MntR); (ii) regulons expanded in the Lactobacillaceae (HrcA and ScrR), and (iii) regulons with different peripheral sets of genes in the Streptococcaceae and Lactobacillaceae (ArgR, CcpA, CtsR, MalR, NrdR, PerR, PurR and Rex). We prepose that different sets of peripheral genes in the above 16 regulons have appeared due to independent expansion of regulons in each lineage. A common core of the ArgR regulon is formed by genes for the arginine transport and biosynthesis. The periphery of this regulon in the

Streptococcaceae consists of the argR gene and arcABC

operon for arginine catabolism, whereas the extended part of the ArgR regulon in the Lactobacillaceae includes only carbamoyl-phosphate synthase (carAB). The PurR regulon in the Streptococcaceae is expanded by genes for metabolism of folate-associated one-carbon compounds, whereas in the Lactobacillaceae this regu-lon has additional genes for the adenine and guanine metabolism (purB and guaB) and ribose-phosphate pyro-phosphokinase (prsA). Expansion of the PerR regulon in the Streptococcaceae affects genes for the iron and man-ganese homeostasis, whereas in the Lactobacillaceae PerR regulates several additional genes such as a NADH peroxidase. The CcpA regulon demonstrated the largest peripheral sets of genes in the studied lineages, 117 operons in the Streptococcaceae and 42 operons in the Lactobacillaceae. Noteworthy, the CcpA regulon in the

Streptococcaceae is expanded by the CRISPR-Cas

cas-sette genes (see above) and some virulence genes (the exfoliative toxin A gene shetA and the streptolysin S

biosynthesis operon sagABCEFGHI), suggesting that this global regulon supplies the link between the carbohy-drate utilization, virulence and anti-phage immunity. Non-orthologous displacements of TF regulons

Functional analysis of reconstructed TF regulons in the Lactobacillales revealed that many biological processes are regulated by two or more non-orthologous TFs. These metabolic subsystems with redundant TF regula-tion include 8 distinct sugar utilizaregula-tion pathways (see above) and the zinc homeostasis that are controlled by 20 non-orthologous TFs (Additional file 5). In most cases, patterns of phylogenetic distribution of non-orthologous TFs controlling the same biological subsys-tem complement each other in the analyzed 30 genomes. Interestingly, there are only four cases in which the non-orthologous TFs belong to different protein families (CelR/CelQ, GntR1/GntR2, LacR/LacR2 and AdcR/Zur), whereas in all remaining cases the identified pairs of non-orthologous TFs belong to the same TF family and thus can be classified as cases of xenologous gene re-placement [44]. The largest number of xenologous repla-cements of TFs was identified within the LacI family (Additional file 8).

Interconnectivity in reconstructed TRNs

The cross talk between TF regulons can be identified by prediction of TFBSs for two or more TFs within the regula-tory region of the same operon. Numerous target operons in the reconstructed TRNs are subject to regulation by multiple TFs. For instance, S. preumoniae has 38 target operons that share TFBSs for two or more TFs. Regulation of several target operons by multiple TFs is evolutionary conserved across a number of related genomes (Table 3).

Three global regulons, CcpA, CodY and Rex, often inter-connect with each other and also with multiple local TF regulons. For example, the CcpA regulon overlaps with 31 TF regulons that control the carbohydrate utilization such as MalR, ScrR, TreR, GalR, FruR, and RegR. The CodY reg-ulon overlaps with the GlnR and CmbR regreg-ulons that con-trol the nitrogen metabolism and sulfur amino acid biosynthesis, respectively. A similar situation was previously observed in S. aureus, where the CcpA regulon overlaps with numerous local regulons for sugar utilization and the CodY regulon overlaps with amino acid metabolism regu-lons [12,45,46]. Similarly, co-regulation of two heat shock response regulons, HrcA and CtsR, was found both in the Streptococcaceaeand Lactobacillaceae groups, and was also previously observed in S. aureus [12,47].

Autoregulation of TFs is a regular feature of the reconstructed regulons. An average portion of the LactobacillalesTFs with predicted control of their own expression is 72%. This index slightly varies between the analyzed genomes and is very close to the percentage of

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autoregulated TFs that was previously reported for S. aureus[12].

Multiple regulatory cascades between various TFs were detected in the studied genomes. For instance, CcpA regu-lates the codY gene and multiple genes encoding sugar utilization regulators, such as FruR, ScrR, TreR, and CelR. Among other identified cascades, CodY controls glnR, CmbR controls homR and PerR controls mntR in the Streptococcaceae, whereas CtsR controls hrcA in the Lacto-bacillaceae. Some cascades, such as the regulation of fruR, galR, gutR, mtlR, scrR and treR genes by CcpA, are con-served between the Streptococcaceae and Lactobacillaceae lineages, whereas the remaining cases of regulatory TF cas-cades are lineage specific. Comparison with the previously reconstructed regulatory network of S. aureus [12] showed that some of the identified Lactobacillales regulatory cas-cades are also conserved in the Staphylococcaceae. For in-stance, the CcpA-dependent regulation of fruR, rbsR2, scrR and treR, as well as the regulation of glnR by CodY and hrcAby CtsR, are conserved between the two groups. The conservation of cascades between distantly related genomes

points to the importance of these regulatory interactions in the Lactobacillales regulatory networks.

Conclusions

The knowledge-based bottom-up approach and com-parative genomics techniques have been previously suc-cessively applied for reconstruction of bacterial TRNs in different groups of genomes [11,12]. Here we tentatively defined the reference collection of TF regulons in 30

Lactobacillales genomes comprised of 102 orthologous

groups of TFs and ~4100 regulatory interactions (~140 per genome). The resulting regulatory network contains ~7500 regulated genes (~250 per genome) that are involved in sugar utilization, stress response, metal homeostasis and metabolisms of amino acids, fatty acids, nucleotides, and cofactors. We used a modified workflow for TRN recon-struction that is characterized by three main innovations: (1) analysis of two taxonomically related groups of genomes (the Streptococcaceae and Lactobacillaceae), (2) involve-ment of numerous experiinvolve-mental data from the literature about TF regulation in lactic acid bacteria, and (3) Table 3 Examples of target operons regulation by multiple TFs

Combinations of TFs Examples

Target operon Genome(s)

Quadruple regulation

AdcR, CcpA, CodY, Rex adhB1 S. agalactiae, S. dysgalactiae

Triple regulation

AdcR, CodY, Rex adhB1 S. equi, S. mitis, S. pyogenes

MtaR, CmbR, HomR metEF S. gallolyticus, S. gordonii, S. mutans, S. pneumoniae

CcpA, MalR, MdxR malXCDA L. plantarum

CcpA, MalR3, MdxR nplT L. plantarum

CcpA, MalR, MdxR malT-mapA-pgmB L. mesenteroides

Double regulation

CcpA, Rex adhE S. mitis, S. sanguinis, L. acidophilus, L. brevis,L. johnsonii

forT S. dysgalactiae, S. equi, S. pyogenes, S. sanguinis, S. uberis

CcpA, MalR ptsG-rgfB S. agalactiae, S. equi, S. gordonii, S. mutans, S. uberis

pulA S. agalactiae, S. equi, S. gordonii, S. mutans, S. pyogenes

HrcA, CtsR groSL all Streptococcaceae, L. fermentum, L. reuteri, P. pentosaceus

hrcA-grpE-dnaKJ L. fermentum, L. plantarum, L. reuteri, L. sakei, P. pentosaceus

CcpA, ScrR scrBR S. gallolyticus, S. gordonii, L. acidophilus, L. johnsonii,

CcpA, TreR trePA S. dysgalactiae, S. pneumoniae, L. mesenteroidesm, O. oeni

CcpA, GalR galKETRM L. casei, L. rhamnosus

lacLM L. fermentum, L. plantarum, L. salivarius, P. pentosaceus

CcpA, CodY livKHMGF S. gordonii, S. mitis, S. sanguinis, S. suis, S. thermophilus

CodY, GlnR gluQHMP S. agalactiae, S. gallolyticus, S. mutans

CcpA, FruR fruRBA L. lactis, S. equi, S. suis, L. helveticus, L. sakei, P. pentosaceus

PerR, MntR mntABC S. equi, S. pyogenes, S. mutans, S. sanguinis, S. uberis

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coordinated reconstruction of multiple TF regulons by a community of annotators using multi-user web interface of the RegPredict tool for regulon analysis [18]. By utilizing the semi-automatic workflow for regulon inference com-bined with manual curation and regulon annotation we described the largest reference collection of TF regulons in lactic acid bacteria do date. The collection also includes the previously uncharacterized regulons for 47 TFs that comprise ~1000 target genes (12– 63 genes per genome). Thus, a significant number of predicted regulatory interac-tions and novel TF regulons await future experimental val-idation. During preparation of this manuscript, Bitoun et al.experimentally analyzed Rex regulon in the S. mutans UA159 [48] and confirmed Rex dependent regulation of 4 targets predicted in this work (adhE, rex-guaA, ldh, and frdCT).

Comparison of the inferred TRNs in the Lactobacillales genomes reveals interesting trends in the evolution of TRNs and individual TF regulons. First, we found a positive correlation between the TFBS motif conservation between the two lactic acid bacteria lineages and the distribution and conservation of the respective TF regulons. Second, we report that in the analyzed collection of Lactobacillales reg-ulons non-orthologous displacements of TFs occur more often between structurally related TFs in comparison with regulators that belong to different protein families.

Methods

Thirty complete genomes of Lactobacillales (Additional file 1) were downloaded from MicrobesOnline database [49]. Primary TF sets for each studied genome were extracted from P2TF (http://www.p2tf.org) database. Sigma factors and RNA binding proteins were excluded from the collections. Groups of orthologs were pre-counted as following. Initially, groups of orthologous proteins were constructed for every pair of TFs sets. All pairwise comparisons were done using BLASTP, and bi-directional best hits (BBHs) were identified if the protein sequences identity was more 50% and the aligned region was longer than 2/3 of the length of the shorter protein. If two paralogous genes from one genome were more similar to each other than to a BBH partner from an-other genome, both paralogs were added to the same orthology cluster. Finally, all orthologous clusters con-taining common genes were joined together. The clus-ters were formed using ad-hoc software written using Oracle RDBMS Express Edition (PL/SQL codes are available by request). TF families were assigned by analysis of protein domain structure using the following databases: CDD [50], Pfam [51], SMART [52], and MicrobesOnline Domain and Families [49].

For regulon reconstruction we used the previously established comparative genomics approach (reviewed in [10]) implemented in the RegPredict Web server

(http://regpredict.lbl.gov) [18]. The approach includes inference of TFBSs, construction of nucleotide positional weight matrices (PWMs) for TFBSs motifs, and recon-struction of regulons in complete genomes on the basis of prediction of putative TFBSs in promoter gene regions. To take into account possible lineage specific changes in TFBSs motifs, we constructed individual PWMs for the Streptococcaceae and Lactobacillaceae taxonomic groups.

The three major workflows used for TF regulon recon-struction are (1) projection and expansion of previously known regulons from model Lactobacillales organisms, (2) projection of known regulons from model organisms belonging to another taxa, and (3) ab initio prediction of novel regulons (Figure 2).

In workflow 1, the projection and expansion of previ-ously know TF regulons includes two slightly different workflows. In workflow 1a, both a set of regulated genes and TFBSs motif are known, whereas in workflow 1b, only a set of co-regulated genes is known from the col-lected experimental data. For previously known TFBSs motifs, a PWM was built and used for identification of additional sites in the analyzed genomes using the Run Profile tool in the RegPredict Web server. All novel true positive TFBSs were added to the training set and the updated PWM was constructed and further used for final regulon reconstruction. For regulons with originally unknown TFBS motifs, we collected a set of upstream regions of known TF-regulated genes and their orthologs and used this set for TFBS identification by the Discover Profile tool in the RegPredict. The TFBS motif discovery tool uses the expectation-maximization algorithm for clustering of all potential motifs with a specified sym-metry (palindrome, direct or inverted repeat) and finally optimizes the inferred PWM. In ambiguous cases, puta-tive regulatory elements were validated by phylogenetic footprinting [53] using multiple alignments for upstream non-coding regions of orthologous genes.

In workflow 2, the previously experimentally studied reg-ulons in other model organisms from the Firmicutes phylum (B. subtilis or S. aureus) were projected to the Lactobacillaceaegenomes. For TFBS identification, we used training sets of upstream regions of genes that are consid-ered as orthologs to the TF-regulated genes from other model species outside of the Lactobacillales lineage.

Workflow 3 was used for ab initio prediction of novel TF regulons. Initially, the presumably co-regulated genes were predicted by the analysis of conserved gene neigh-borhoods around a putative TF gene. Upstream regions of presumably co-regulated genes extracted from multiple Lactobacillales genomes were used for identification of TFBSs and PWM construction as described above.

The obtained PWMs for known or predicted TFBS motifs were used for comparative genomics reconstruction

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of regulons in two groups of genomes, the Lactobacillaceae and Streptococcaceae, using the RegPredict Web server [18]. Each studied genome was scanned with the con-structed PWMs using Run Profile tool in RegPredict. The threshold for site search was defined as a lowest score observed in the training set. The consistency check ap-proach [10,54] and/or functional relatedness of candidate target operons were used to eliminate false positive TFBS predictions.

A community of annotators consisting of 18 under-graduate students from Hope College (Holland, MI) performed initial reconstruction of multiple TF regulons in the analyzed groups of genomes. This represented a coordinated annotation effort with expert curators in the context of a microbiology course. The resulting draft regulons underwent strict quality control to ensure ac-curacy of the reconstructions.

Functional gene annotations were uploaded from SEED [25], UniProt [55] and MicrobesOnline [49]. Multiple align-ments of protein and DNA sequences were built by MUSCLE [56]. Phylogenetic trees were constructed using maximum likelihood algorithm implemented in PHYLIP package (v 3.69) [57] and visualized via Dendroscope tool [58]. Complete description of the reconstructed regulons including TFs, their target genes and operons, and asso-ciated TFBS were uploaded to the RegPrecise database (http://regprecise.lbl.gov) [27].

Additional files

Additional file 1: Phylogenetic tree of 30 studied Lactobacillaceae genomes.- Description of data: Economic impact for each studied genome is shown in square brackets. The tree is based on approximately 78 universal prokaryotic proteins in the MicrobesOnline database: http://www.microbesonline.org/cgi-bin/speciesTree.cgi.

Additional file 2: Repertoire of DNA-binding transcriptional factors identified in 15 Streptococcaceae genomes. Description of data: Orthologous groups are sorted by TF family and then by conservancy of each TF group. TFs with regulons reconstructed in this work are highlighted by light blue.

Additional file 3: Repertoire of DNA-binding transcriptional factors identified in 15 Lactobacillaceae genomes. Description of data: Orthologous groups are sorted by TF family and then by conservancy of each TF group. TFs with regulons reconstructed in this work are highlighted by light blue.

Additional file 4: Distribution of predicted DNA binding transcription factors in studied Lactobacillales genomes. Additional file 5: Collection of TF regulons reconstructed in Lactobacillales. Description of data:aNovel TF names introduced in this work are marked by asterisks.bPresence (+) or absence (−) of TFs orthologs.cWorkflow 1, expansion and projection of a regulon previously characterized in model Lactobacillales organisms: (1a) TFBSs motif was known, (1b) TFBSs motif was predicted in present work; Workflow 2, projection of an orthologous regulon from B. subtilis or S. aureus; Workflow 3, ab initio regulon inference. Regulons previously studied in model organisms highlighted by green. Names of functional groups are highlighted in blue.

Additional file 6: Functional content, experimental evidences and conservation for reconstructed regulons in Streptococcaceae.

Description of data: Regulons are sorted by regulator names. Novel TF names introduced in this work are marked by asterisks.‘Conservation of regulatory interaction’ column shows number of genomes with regulated gene/ operon (number of genomes having orthologs of operon). Additional file 7: Functional content, experimental evidences and conservation for reconstructed regulons in Lactobacillaceae. Description of data: Regulons are sorted by regulator names. Novel TF names introduced in this work are marked by asterisks.‘Conservation of regulatory interaction’ column shows number of genomes with regulated operon (number of genomes having orthologs of operon).

Additional file 8: Analysis of LacI family TFs in the studied Lactobacillales genomes.

Additional file 9: Comparison of predicted binding site motifs inStreptococcaceae and Lactobacillaceae genomes. Description of data:1Sequences Logos were constructed using WebLogo package (http://weblogo.berkeley.edu/logo.cgi).2NS, number of binding site sequences used to construct Logo.3Category reflects a conservancy between TFBS motifs in Streptococcaceae and Lactobacillaceae: I, highly conserved motifs; II, moderately different motifs; III, substantially different motifs.4Number of genomes that contain this regulon.5Average number of target operon in regulon per genome.

Abbreviations

TF: Transcription factor; TFBS: Transcription factor binding site; TRN: Transcriptional regulatory network.

Competing interests

The authors declare that they have no competing interests. Authors’ contributions

DARo and PSN conceived and designed the research project. DARa and DARo wrote the manuscript. DARa, DARo, and NVS performed comparative genomic analysis for reconstruction of regulons. DARo also provided the quality control as a curator. MDK computed orthologous clusters for TFs. AAB contributed to the development of the project, writing of the manuscript, and implemented the community annotation effort at Hope College. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by the Office of Science, Office of Biological and Environmental Research, of the U.S. Department of Energy under contract DE-SC0004999 with SBMRI and LBNL. Additional funding was provided by the Russian Foundation for Basic Research (12-04-33003 and 12-04-31939), State contract #8135 (application 2012–1.2.2-12-000-1013-079) to MDK, and by the National Science Foundation under award DBI-0850546 to AAB and DARo. We would like to thank the students of the Fall 2011 Microbiology course at Hope College for their work to build initial reconstructions of regulons.

Author details

1Sanford-Burnham Medical Research Institute, La Jolla, CA 92037, USA.2A.A.

Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences, Moscow 127994, Russia.3Department of Biology, Hope College,

Holland, MI 49423, USA.4Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA.

Received: 16 November 2012 Accepted: 8 February 2013 Published: 12 February 2013

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doi:10.1186/1471-2164-14-94

Cite this article as: Ravcheev et al.: Genomic reconstruction of transcriptional regulatory networks in lactic acid bacteria. BMC Genomics 2013 14:94.

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